Extraction of vital signs using real time video analysis for neonatal monitoring

摘要

One of the major concerns within the health system in the United States is the higher number of infant deaths (23,215 in 2014). This has triggered the need for intensive care facilities for at-risk infants and especially for neonates. Premature infants in Neonatal Intensive Care Units (NICU) need constant monitoring due to conditions like bradycardia, apnea and hypoxia that can lead to dangerous conditions including death. A contact-less method to record critical vital signs is a subject of interest for neonates as their skin is fragile and damageable by traditional sensors. Video monitoring of infants in NICU can be one of the solutions to this problem. Automated analysis of video feeds as opposed to manual approaches can render promising results. One of the important cues to detect the occurrence of conditions such as bradycardia is the pulse rate of the subject. In this paper, we present an approach to monitor patient's pulse rate using video processing algorithms. A multi-step procedure was designed and tested on several subjects. Video from the frontal facial pose was captured and a region of interest (ROI) was selected. Statistical features such as the gray level average were extracted from the ROI in each frame and plotted as a function of time after Gaussian Smoothing. The feature signals were then de-noised using Maximal Overlap Discrete Wavelet Transform (MODWT). Filter banks tuned to the application were designed using bandpass cutoff frequencies and applied to the signal. The output signal resembled the actual pulse rate to a high degree of accuracy. Using Welch approximation, the Power Spectral Density (PSD) of the output signal was determined to display pulse rate. Further work to perform signal processing steps in the spatial domain is planned so that real time display of pulse rate will be possible.